Abstract — This paper describes an interactive application we have developed based on shaped-basedimage retrieval technique. The key concepts described in the project are, i)matching of images based on contour matching; ii)matching of images based on edge matching; iii)matching of images based on pixel matching of colours. Further, the application facilitates the matching of images invariant of transformations like i) translation ; ii) rotation; iii) scaling. The key factor of the system is, the system shows the percentage unmatched of the image uploaded with respect to the images already existing in the database graphically, whereas, the integrity of the system lies on the unique matching techniques used for optimum result. This increases the accuracy of the system. For example, when a user uploads an image say, an image of a mango leaf, then the application shows all mango leaves present in the database as well other leaves matching the colour and shape of the mango leaf uploaded.
Abstract—This paper proposes a novel technique for content-basedimage retrieval based on tree matching. Image objects and their relations are some of the important features to match similar images. This new algorithm segments the image into some specific regions and then extracts their color, size, position, shape and object’s relation. This research, for the first time, employs object distance and minimum spanning tree of image graph for the region and image matching, respectively. The proposed algorithm computes center of each segment and connects center of them to obtain the image graph. By obtaining minimum spanning tree and then tree matching, we compare the image that is being searched in the database against the sample image. The simulation results are achieved from performing this method on an image database containing 10000 images and the superiority of proposed algorithm has been proved in terms of recall rate and precision rate.
Gayubo, et al. (2013) developed a MATLAB module to track automatically labelled RBCs flowing through a microchannel. Using fluorescent labelling, several individ- ual RBCs were able to track automatically as bright object against a darker background. However, this method still requires some consuming time by the user to perform tracking and is not able to measure RBCs deformability flowing along the microchannel. Hence, it is crucial to develop a fast automatic method that is able to not only track individual RBCs but also measure their deformability. In this study, we propose an automatic image analysis techniquebased on the keyhole model to characterise the motion and deformation of RBCs flowing through a microchannel having a smooth contraction shape. In this geometry, the mechanical properties of RBCs are under the effect of both simple shear and extensional flows.
This paper presents new methodologies to automatically extract significant points, from an object represented in images, useful to construct Point Distribution Models. Each model consists of a flexible shape template, describing how significant points of the object can vary, and a statistical model of the expected grey levels in regions around each model point. This information can be used to search objects in new images: Active Shape and Active Appearance Models. Both use PDMs for image analysis, to locate structures modeled in new images, or in a classifier, an estimate can be made of how likely the example in cause is a member of the class of shapes described by the model build. We present results for two objects: a hand and a face.
From the last decade, the multi-scale image segmentation is getting a particular interest and practically being used for object-basedimage analysis. In this study, we have addressed the issues on multi-scale image segmentation, especially, in improving the performances for validity of merging and variety of derived region’s shape. Firstly, we have introduced constraints on the application of spectral criterion which could suppress excessive merging between dissimilar regions. Secondly, we have extended the evaluation for smoothness criterion by modifying the definition on the extent of the object, which was brought for controlling the shape’s diversity. Thirdly, we have developed new shape criterion called aspect ratio. This criterion helps to improve the reproducibility on the shape of object to be matched to the actual objectives of interest. This criterion provides constraint on the aspect ratio in the bounding box of object by keeping properties controlled with conventional shape criteria. These improvements and extensions lead to more accurate, flexible, and diverse segmentation results according to the shape characteristics of the target of interest. Furthermore, we also investigated a technique for quantitative and automatic parameterization in multi-scale image segmentation. This approach is achieved by comparing segmentation result with training area specified in advance by considering the maximization of the average area in derived objects or satisfying the evaluation index called F-measure. Thus, it has been possible to automate the parameterization that suited the objectives especially in the view point of shape’s reproducibility.
The second approach takes a different stand and treats images and texts as equivalent data. It attempts to discover the correlation between visual features and textual words on an unsupervised basis, by estimating the joint distribution of features and words and posing annotation as statistical inference in a graphical model. For example image retrieval system based on decision trees and rule induction was presented in  to annotate web image using combination of image feature and metadata, while in , a system that automatically integrate the keyword and visual features for web image retrieval by using association rule mining technology. These approaches usually learn the keywords correlations according to the appearance of keywords in the web page, and the correlation may not reflect the real correlation for annotating Web images or semantic meaning of keywords such as synonym . Ontology-basedimage retrieval is an effective approach to bridge the semantic gap because it is more focused on capturing semantic content which has the potential to satisfy user requirements better [10,11]. While semantically rich ontology addresses the need for complete descriptions of image retrieval and improves the precision of retrieval. However, the lack of text information which affects the performance of keyword approach is still a problem in text ontology approach. Ontology works better with the combination of image features .this paper presents a new framework for web image retrieval search engine which relay not only on ontology to discover the semantic relationship between different keywords inside the web page but also propose a new voting annotation technique extract the shared semantically related keywords from different web pages to eliminate and solve the problem of subjectivity of image annotation of traditional approaches and enhance the performance of the retrieval results by taking the semantic of the correlated data into consideration.
Darshana Mistry  has proposed that in content BasedImage Retrieval, images were retrieved based on color, texture and shape (low level perception). There was a gap between user semantics (high level perception) and low level perception. Relevance feedback (RF) learns association between high level semantics and low level features. Bayesian method, nearest neighbour search method, Log based RF; Support Vector Machine (SVM) was methods of Relevance Feedback. Bayesian method was good for understand but it has been not worked for fast access. Survey of different methods of Relevance feedback, SVM has been the best method because of structure risk minimization. Using of SVM, Using of relevance feedback with SVM, results were more efficient as user perception. SVM classification could be even better if the feature vector used in more relevant to images.
Content basedimage retrieval (CBIR) is an effective method of retrieving images from large image resources. CBIR is a technique in which images are indexed by extracting their low level features like, color, texture, shape, and spatial location, etc. Effective and efficient feature extraction mechanisms are required to improve existing CBIR performance. This paper presents a novel approach of CBIR system in which higher retrieval efficiency is achieved by combining the information of image features color, shape and texture. The color feature is extracted using color histogram for image blocks, for shape feature Canny edge detection algorithm is used and the HSB extraction in blocks is used for texture feature extraction. The feature set of the query image are compared with the feature set of each image in the database. The experiments show that the fusion of multiple features retrieval gives better retrieval results than another approach used by Rao et al. This paper presents comparative study of performance of the two different approaches of CBIR system in which the image features color, shape and texture are used.
Magnetic Resonance (MR) image is a medical imagetechnique required enormous data to be stored and transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the performance of the compression scheme. In this paper we extended the commonly used algorithms to image compression and compared its performance. For an image compression technique, we have linked different wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram of the image targeted is introduced to assess image compression quality. The index will be used in place of existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about the distortion between an original image and a compressed image in comparisons with UIQI. The proposed index is designed based on modelling image compression as combinations of four major factors: loss of correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate and applicable in various image processing applications. One of our contributions is to demonstrate the choice of mother wavelet is very important for achieving superior wavelet compression performances based on proposed image quality indexes. Experimental results show that the proposed image quality index plays a significantly role in the quality evaluation of image compression on the open sources “BrainWeb: Simulated Brain Database (SBD) ”.
In the next step the arrays of image IDs (A, B and C) are sorted based on the number of appearances (ranking) of image IDs in each array. The number of repeating image IDs in the appropriate group of features indicates the similarity to the query image by various criteria. Because of a number of the coordinates and their variance there is a problem with the dominance of certain characteristics when the Euclidean distance between feature vectors is used as the only criterion for similarity in the initial search. This problem is overcome with the indication of similarity by various criteria in the proposed algorithm. After sorting, N2 = 15 image files with the highest number of appearances are selected from each group of features (A,B and C), and moved with a preserved rank order to a new set of arrays COLOR,TEXTURE and SHAPE respectively, as it is shown in Fig. 2.
The average size of speckles and its distribution in the random pattern plays an important role in these similarity-searching algorithms. It was found, through testing that the speckles should be two to three pixels in size, when imaged by the video camera, in order to achieve satisfactory correlation results by using the coarse- fine search method (Bruck et al., 1989). Nevertheless, the influence of the average speckle size is expected to be more significant in the FAS algorithm because it lacks the ability to adjust the dimensionality or shape of the target window with respect to the corresponding deformation. Therefore an investigation of strains against different speckle sizes was performed. Figure 12 shows the calculated result of a normal strain εx with respect to different average speckle sizes in the uniaxial tension test. The variety in average speckle size was obtained by equally resizing both dimensions of the original pattern of the acquired image, either by dezooming or zooming. The bicubic interpolation process was adapted for zooming the pattern, while the averaging method was used for dezooming. The average speckle size was sampled from each image and calculated using digital image processing procedures (Matrox Image Processing, 1997). The speckle size analysis was performed by binarizing the images, based on their thresholds, and then measuring and averaging speckle diameters at various cross-sections.
Image retrieval is one of the efficient and new problems in image processing area. The main aim in image retrieval is retrieving the most similar images to the query image from a huge database. In the direction of achieving this aim, researchers try to define the most effective and meaningful features to compare images. Yet, various methods have been proposed for image retrieval. In  a method based on energy compaction has been given. In a recent work Ershad  described a method based on primitive pattern units. In  some features based on texture analysis has been given. Vasconcelos  has proposed a method according to the features of shape. In a large set of methods presented so far, color histogram analysis of image has been used to define features. One of the approaches which had some desirable results is color basedimage retrieval (CBIR) . In this method for comparing the similarity rate of two image, firstly the histogram of two images would be individually extracted in each color channel Red, green and blue and then compared by a criterion like Euclidean one . Equation (1) represents this affair.
All three of these standards employ a basic technique known as the discrete cosine transform (DCT), which is developed by Ahmed, Natarajan, and Rao . It is a lossless compression technique. The DCT is usually applied to reduce spatial redundancy in order to achieve good compression performance. Some of the applications of two-dimensional DCT technique involve image compression and compression of individual video frames. DCT is also useful for transferring multidimensional data from spatial domain to frequency domain, where different operations, like spread spectrum, data compression, data watermarking can be performed in performed manner. The JPEG process is a widely used form of lossy image compression that centers on the Discrete Cosine Transform. DCT and Fourier transforms convert images from spatial-domain to frequency-domain to decorrelate pixels. The JPEG is used for both color and black and-white images.
Medical imaging has become one of the most important clinical diagnosis components. Medical images are different from general images in the sense that they contain anatomical expertise. However, general registration techniques do not well exploit such expertise so better extraction and matching techniques are required for precise computer assisted diagnosis. Content-basedimage retrieval (CBIR) of medical images is an important alternate and complement to traditional text-based retrieval using keywords. The purpose of this Paper is to describe our research on improvement of feature extraction and matching technique for more precise and accurate Content BasedImage Retrieval (CBIR) system specially designed for Brain scan images regarding various brain diseases and abnormalities.
A multikernel sparse representation–basedimage classification implementation was implemented, and the results were tabulated. The results showed that the accuracy of the MKSR-based implementation is on average better than the SVM and ICDA implementations, and further improvement of the kernel features can result in a robust method with higher accuracy. Given the robust nature of the algorithm used, the implementation on images with lesser illumination also performed better as the HOG and CH kernel spaces were fused. The template matching, if conducted using a variable size template, could further improve the accuracy. The computational complexity was considerably reduced in the proposed MKSR method by overcoming the size variation drawback of SVM and the density estimation variation of ICDA with the combined use of PFF and multikernel optimisation technique.
Abstraction is nothing but the biggest merit and demerit of the kinship ,since abstraction allows kinship to be pervasive and removes the knowledge of underlying storage and social media security of images to strengthen the social media environment, But such abstraction keeps the information *owner unnoticed about underlying knowledge of kinship hence the phenomenon of securing database application and information becomes very complex. for information owners. Many traditional kinship database principle used today to identify relationship data and social networking site depends upon the information owners ability to manage the underlying blood relationship and intrusion detection system to become aware about when peoples are missed and to counter to the people gather together by preventing kinship to the resources and isolating pieces or subsets of the image that are being people are missed
The methodology used to compute all the optimal solutions of problem (1), hereafter called MCSFilter method, is a multistart algorithm coupled with a clustering technique to avoid the convergence to previously detected solutions. The exploration feature of the method is carried out by a multistart strategy that aims at generating points randomly spread all over the search space. Exploitation of promising regions is made by a simple local search approach. In contrast to the line search BFGS method presented in , the local search proposal, a crucial procedure inside a multistart paradigm, relies on a direct search method, known as coordinate search (CS) method , that does not use any analytical or numerical derivative information.
tactile sensor consists of a photo reflector covered by urethane foam and organized as a network of self- contained module that communicates through a serial bus. A research team at Nagoya University developed a novel optical three axes tactile sensor system based on an optical waveguide transduction method capable of acquiring normal and shear force (Ohka et al., 2004). Fath El Bab et al. (2009) uses information from a micro-machined piezoresistive type tactile sensor to detect the compliance (reciprocal of stiffness) of a soft tissue in order to help the surgeon to determine the health of a tissue. Petropoulos et al. (2009) fabricated a capacitive type tactile sensor using copper clad laminated with flexible polyimide substrates (Kapton). Polster and Hoffmann (2009) proposed a tactile sensor based on tridimensional piezoelectric Aluminum Nitride (AIN) membranes. By analyzing strengths and weaknesses of the above mentioned researches, a new tactile sensor based on a silicone material combined with image analysis technique was proposed in this study.
4 As a result, a growing number of empirical studies have sought to measure investor sentiment. Traditionally, empiricists have taken two approaches to measuring investor sentiment as most of these studies have identified direct and indirect sentiment measures (see Qiu and Welch  for a literature review). While direct sentiment measures are derived from surveys asking individuals how they feel about stock market conditions and current or future economic conditions, indirect sentiment measures represent economic and financial variables that seek to capture investors’ state of mind. In recent years, innovative measures have been proposed that can handle the latest technological developments and consumers’ social media usage patterns (Piñeiro- Chousa, López-Cabarcos & Pérez-Pico, 2016). According to Ho, Damien, Gu and Konana (2017, p. 69), the ‘wisdom of the crowd provides market sentiments that can be a proxy for the market mood’. One measure, for example, relies on data on Internet search frequency by household members, as suggested by Beer, Herve and Zouaoui (2013); Da, Engelberg and Gao (2015) and Preis, Moat and Stanley (2013).
We demonstrate that chitosan-based porous scaffolds can present a shape memory effect triggered by hydration. The shape memory effect of non-crosslinked (CHT0) and genipin-crosslinked (CHT1) scaffolds was followed by hydromechanical compressive test and dynamic mechanical analysis (DMA), while the sample was immersed in varying compositions of water/ethanol mixtures. By dehydration with higher contents of ethanol, the vitreous-like nature of the amorphous component of chitosan allows the fixation of the temporary shape of the scaffold. The presence of water disrupts inter-molecular hydrogen bonds permitting segmental mobility of the chitosan chains upon the occurrence of the glass transition and thus the recovery of the permanent shape of a pre-deformed scaffold. SEM, swelling tests and XRD analysis were also performed. Results showed that chitosan possess shape memory properties, characterized by a fixity ratio above 97.2% for CHT0 and above 99.2% for CHT1 and a recovery ratio above 70.5% for CHT0 and 98.5% for CHT1. In vitro drug delivery studies were also performed to demonstrate that such devices can be also loaded with molecules. We show that the developed chitosan scaffolds are candidates for applications in minimally invasive surgery for tissue regeneration or for drug delivery.